Crystal Structure Prediction by Bayesian Optimization and Evolutionary Algorithm
ORAL
Abstract
Crystal structure prediction methods such as random search (RS) and evolutionary algorithm (EA) have attracted attention. Previously we have developed a searching algorithm accelerated by Bayesian optimization (BO). BO is a selection-type algorithm which can efficiently select potential candidates by machine learning. First, we compared searching efficiency among RS, EA, and BO in the small system of Si16. In each algorithm, a hundred structures were searched. The importance of random generation is found compared with evolutionary operations even in EA. RS could be the most efficient for small systems. Furthermore, we develop a hybrid algorithm of BO and EA, and discuss the searching efficiency in large systems.
*This work was supported in part by Materials research by Information Integration Initiative (MI2I) project of the Support Program for Starting Up Innovation Hub from Japan Science and Technology Agency (JST), by Japan Society for the Promotion of Science (JSPS) KAKENHI Grant Number JP 18K13474, and by Ministry of Education, Culture, Sports, Science and Technology (MEXT) of Japan as a social and scientific priority issue (Creation of new functional devices and high-performance materials to support next-generation industries; CDMSI) to be tackled by using a post-K computer.
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Presenters
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Tomoki Yamashita
- National Institute for Materials Science